Intelligent Automation For Adaptive Energy Management Using Iot, Machine Learning, and Edge Computing

Abstract

The rapid growth of connected infrastructure and energy-intensive systems has increased the need for intelligent, real-time, and adaptive energy management solutions. Conventional energy management systems primarily rely on centralized monitoring and predefined control rules, which often lack the capability to respond effectively to dynamic variations in energy demand, environmental conditions, occupancy patterns, and equipment behavior. This paper proposes an Intelligent Automation Framework for Adaptive Energy Management using Internet of Things (IoT), Machine Learning (ML), and Edge Computing. The proposed methodology consists of four integrated layers: an IoT-based sensing layer, an edge computing layer, a machine learning analytics layer, and an intelligent automation layer. IoT sensors continuously acquire real-time data related to energy consumption, temperature, humidity, occupancy, equipment operating status, and environmental conditions. The collected data are preprocessed and analyzed at edge devices to perform data filtering, normalization, anomaly detection, and low-latency decision-making. Machine learning models are employed to forecast short-term energy demand, classify consumption patterns, and identify abnormal energy usage. Based on predictive insights and predefined optimization constraints, the automation layer dynamically controls connected electrical devices and adjusts their operating schedules according to real-time conditions. The proposed framework is expected to reduce unnecessary energy consumption, minimize response latency, decrease dependence on cloud infrastructure, and improve overall energy utilization efficiency. Experimental evaluation is proposed using metrics such as energy consumption reduction, prediction accuracy, response time, computational latency, and system reliability. The anticipated results demonstrate that integrating IoT sensing, ML-based prediction, and edge intelligence can provide a scalable, autonomous, and adaptive solution for smart homes, commercial buildings, industrial environments, and smart city applications.